• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.11 no.4
• /
• pp.786-792
• /
• 2007

Moving object tracking is very important in video surveillance system. This paper presents a method for tracking moving objects in an outdoor environment. To moving object tracking, first, after extract object that move yielding weight subtraction image and then use close operator to reduce the noise. And we track a object that move detected by matching the extracted multi-feature information. The proposed tracking technique can track moving object by multi-feature matching method so that exactly tracking the objects which are suddenly move or stop. The proposed tracking technique can be efficiently tracking the moving objects, because of combined with spatial position, shape and intensity informations.

• Journal of the Korean Society for Precision Engineering
• /
• v.20 no.8
• /
• pp.115-125
• /
• 2003

To analyze the applications of all types of mechanical systems, general purpose analysis programs have been developed and commercialized. However, it is customary to develop and use customized programs even though they sometimes require more work than a general purpose program. A customized program is simplified to adapt to a particular application from the beginning, is designed for small computers, and developed with hardware-in-the-loop in mind so it can be applied effectively. By adding design knowledge and bundling know-how to an analysis program, analysis time can be reduced. And because an analysis has to work in conjunction with other analysis programs, a proprietary program that the user can easily modify can be useful. In this thesis, a multi-body dynamics analysis system is presented using one of the most useful programming techniques, object-oriented concept. The object-oriented concept defines a problem from the physical world as an abstract object, an abstract model. The object becomes encapsulated with the data and method. Simulation is performed using the object's interface. It is then possible for the user and the developer to modify and upgrade the program without having particular knowledge of the analysis program. The method presented in this thesis has the following advantages. Since the mechanical components of the multi-body system converts independent modeling into a class, the modification, exchange, distribution, and reuse of elements are increased. It becomes easier to employ a new analysis method and interface with other S/W and H/W systems. To employ a new analysis method, there is no need to modify elements of the main solver and the Library. In addition, information can be communicated to each object through messaging. It makes the modeling of new elements easier using inheritance. When developing a S/W for the computer simulation of physical system, it is reasonable to use object-oriented modeling. Also, for multi-body dynamics analysis, it is possible to develop a solver that is user-oriented.

• Proceedings of the IEEK Conference
• /
• 2008.06a
• /
• pp.947-948
• /
• 2008

Recently, many efforts have been made for research and application of object tracking system. However, introduced object tracking algorithms have limitations to adopt a realtime object tracking system with multi camera. In this paper, we present a novel background generation and target object recognition algorithm for realtime object tracking system with multi camera and implemented it.

• International Journal of Advanced Culture Technology
• /
• v.6 no.3
• /
• pp.142-150
• /
• 2018

This paper describes the algorithm that lowers the dimension, maintains the object recognition and significantly reduces the eigenspace configuration time by combining the edge orientation histogram and principle component analysis. By using the detected object region as a recognition input image, in this paper the object recognition method combined with principle component analysis and the multi-layer network which is one of the intelligent classification was suggested and its performance was evaluated. As a pre-processing algorithm of input object image, this method computes the eigenspace through principle component analysis and expresses the training images with it as a fundamental vector. Each image takes the set of weights for the fundamental vector as a feature vector and it reduces the dimension of image at the same time, and then the object recognition is performed by inputting the multi-layer neural network.

• Journal of Multimedia Information System
• /
• v.9 no.3
• /
• pp.183-190
• /
• 2022

Image-based 3D object detection is one of the important and difficult problems in autonomous driving and robotics, and aims to find and represent the location, dimension and orientation of the object of interest. It generates three dimensional (3D) bounding boxes with only 2D images obtained from cameras, so there is no need for devices that provide accurate depth information such as LiDAR or Radar. Image-based methods can be divided into three main categories: monocular, stereo, and multi-view 3D object detection. In this paper, we investigate the recent state-of-the-art models of the above three categories. In the multi-view 3D object detection, which appeared together with the release of the new benchmark datasets, NuScenes and Waymo, we discuss the differences from the existing monocular and stereo methods. Also, we analyze their performance and discuss the advantages and disadvantages of them. Finally, we conclude the remaining challenges and a future direction in this field.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2009.01a
• /
• pp.85-88
• /
• 2009

In this paper, we propose a DRF-based object detection method using the object adaptive patch in the satellite imagery. It is a Discriminative Random Fields (DRF) based work, so the detection is done by labeling to the possible patches in the image. For the feature information of each patch, we use the multi-scale and object adaptive patch and its texton histogram, instead of using the single scale and fixed grid patch. So, we can include contextual layout of texture information around the object. To make object adaptive patch, we use "superpixel lattice" scheme. As a result, each group of labeled patches represents the object or object's presence region. In the experiment, we compare the detection result with a fixed grid scheme and shows our result is more close to the object shape.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.4 no.2
• /
• pp.165-171
• /
• 2004

Robots will be able to coexist with humans and support humans effectively in near future. One of the most important aspects in the development of human-friendly robots is to cooperation between humans and robots. In this paper, we proposed a method for multi-object identification in order to achieve such human-centered system and robot localization in intelligent space. The intelligent space is the space where many intelligent devices, such as computers and sensors, are distributed. The Intelligent Space achieves the human centered services by accelerating the physical and psychological interaction between humans and intelligent devices. As an intelligent device of the Intelligent Space, a color CCD camera module, which includes processing and networking part, has been chosen. The Intelligent Space requires functions of identifying and tracking the multiple objects to realize appropriate services to users under the multi-camera environments. In order to achieve seamless tracking and location estimation many camera modules are distributed. They causes some errors about object identification among different camera modules. This paper describes appearance based object representation for the distributed vision system in Intelligent Space to achieve consistent labeling of all objects. Then, we discuss how to learn the object color appearance model and how to achieve the multi-object tracking under occlusions.

• Smart Media Journal
• /
• v.13 no.5
• /
• pp.9-18
• /
• 2024

Multi-Object trajectory modeling is a major challenge in MOT. CenterTrack tried to solve this problem with a Heatmap-based method that tracks the object center position. However, it showed limited performance when tracking objects with complex movements and nonlinearities. Considering the degradation factor of CenterTrack as the dynamic movement of pedestrians, we integrated the EKF into CenterTrack. To demonstrate the superiority of our proposed method, we applied the existing KF and UKF to CenterTrack and compared and evaluated it on various datasets. The experimental results confirmed that when EKF was integrated into CenterTrack, it achieved 73.7% MOTA, making it the most suitable filter for CenterTrack.

• Proceedings of the IEEK Conference
• /
• 2005.11a
• /
• pp.1165-1168
• /
• 2005

The video device is used to gain lots of informations in indoor environment. The one of informations is the information to identify the moving object. The methods to identify the moving object are to recognize the face, the gait and to analyze the hue histogram of the clothes. The hue data is effective at the environment of multi-video. In this paper, we describe the existing research about to identify the moving object in the environment of multi-video and find its problems. finally, we present the enhanced methods to solve its problems. In the future, the method will be use for recognizing the location of object in ubiquitous home.

• International journal of advanced smart convergence
• /
• v.9 no.3
• /
• pp.85-96
• /
• 2020

It is important to extract salient object image and to solve the invariance problem for image recognition. In this paper we propose a new hybrid algorithm for invariance and improved classification performance in image recognition, whose algorithm is combined by FT(Frequency-tuned Salient Region Detection) algorithm, Guided filter, Zernike moments, and a simple artificial neural network (Multi-layer Perceptron). The conventional FT algorithm is used to extract initial salient object image, the guided filtering to preserve edge details, Zernike moments to solve invariance problem, and a classification to recognize the extracted image. For guided filtering, guided filter is used, and Multi-layer Perceptron which is a simple artificial neural networks is introduced for classification. Experimental results show that this algorithm can achieve a superior performance in the process of extracting salient object image and invariant moment feature. And the results show that the algorithm can also classifies the extracted object image with improved recognition rate.